Techniques of optical object recognition have applications in a variety of fields, including automated manufacturing, biomedical engineering, security, and document analysis.
Most object recognition methods measure the similarity between two objects based on a set of features extracted from each of the objects. These methods can be classified into three types—Global feature methods, structural feature methods, and relational graph methods—explained in “Model-Based Recognition in Robot Vision” (R. T. Chin and C. R. Dyer, Computing Surveys, Vol.18, No.1, March 1996).
In global feature methods, the object is represented by a feature vector containing n global features of the object. Thus, each object can be viewed as a point in an n-dimensional feature space, and two objects can be compared using a function of their corresponding points in the feature space, such as a measure of distance.
An application usually associated with global methods is object identification, which consists of recognizing an isolated object (target object) contained in an image as one in a bank of predefined model objects. Typically, the object identification process comprises two main phases: model definition and object recognition. During the model definition phase, each of the model objects is represented by a feature vector according to a chosen feature extraction scheme. During the object recognition phase, an image is acquired (e.g. using a gray-scale digital camera, etc.) and pre-processed to isolate individual objects. For one of these isolated objects, the target object, a feature vector is extracted according to the chosen feature extraction scheme. This feature vector is then compared to the feature vectors of the model objects; each target-model pair is attributed a similarity score.
In object identification applications, the model object as it occurs in the target image may have undergone an affine transformation (e.g. translation, rotation, scale variation, aspect-ratio variation, skew, etc.) and degradation of various types (e.g. erosion and dilatation, addition or subtraction of pieces, etc.). For gray scale images, the model object may have been affected by a change in image contrast or mean gray level.
Therefore, object identification must support the set of affine transformations and be robust to the types of degradation expected for the specific application. Support for the transformations can be achieved by including in the pre-processing stage one or more standardization steps during which the object is roughly centered in the sub-image and normalized to a standard size and orientation; and/or by selecting features that are invariant under the transformations; and/or by choosing a similarity measure that detects the correlation between the vectors of two objects related through an affine transformation.
In global feature methods, the process of feature extraction consists of extracting from the object a set of features adequate for identification purposes, namely a set of features that vary across the model objects and thus enable their differentiation. Various types of features are used in object recognition, as described in “Feature Extraction Methods for Character Recognition—A Survey” (Ø. D. Trier, A. K. Jain and T. Taxt, Pattern Recognition, Vol. 29, No. 4, pp. 641-662, 1996).
A particular class of features used for object recognition are those derived from geometric moments of the object, as described in Moment Functions in Image Analysis (R. Mukundan and K. R. Ramakrishnan, World Scientific Publishing, 1998). Geometric moments of different orders of the object provide different spatial characteristics of the intensity distribution within the object (and of the mass distribution of the object for binary images); for example, moments of order zero and one together provide elementary object descriptors such as the total intensity and the intensity centroid of the object (total mass and center of mass for binary images).
Geometric moments have many desirable characteristics for use as features in object recognition. Central moments that are calculated by shifting the origin of the reference system to the intensity centroid are invariant to translation. Furthermore, geometric moments can be combined to obtain moment features that are invariant to other transformations such as uniform scale variation, aspect-ratio variation and rotation, called geometric moment invariants.
In typical moment-based methods of object recognition, an object is represented by a set of features derived from global moments of the object of various orders. Increasing the order of the moments used provides a larger set of features to represent the object, but also results in a greater sensitivity to noise.
An alternative feature set that does not require the use of higher order moments uses local moments of the object, as opposed to global moments. In a method known as “Zoning” applied to binary images, an n×m (uniform rectangular) grid is superimposed on the object image, and the masses (zero-order moments) of the object in each of the n×m regions are used as features. However, as the outer boundary of the grid is fixed by the outmost points of the object, the addition or subtraction of a piece, even of negligible mass relative to the object, can significantly alter the grid and therefore the extracted features.
Another method based on local moments is described in “Scene Classification by Fuzzy Local Moments” (H. D. Cheng and R. Desai, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 12, No. 7, 921-938,1998). The method locates the intensity centroid of the object, on which it centers an n×m radial grid; the outer boundary of the grid, the nth concentric circle, is fixed by the object point at the greatest radial distance from the center of mass. The features are the first-order radial moments calculated in each of the n×m regions. This method suffers from the same disadvantage as zoning, namely that the size and position of the grid is fixed by the outer boundary of the object, which is vulnerable to degradation. Also, using a radial system of axes is not computationally efficient.
There is therefore a need in the industry for an object recognition method that will overcome the above-identified drawbacks.